面向低重力行星探测的强化学习行走、跳跃与飞行姿态控制
Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control
AI总结：
本文利用强化学习为四足机器人在火星低重力环境下开发行走、垂直跳跃、前向跳跃及飞行姿态控制策略，实现跨越障碍物并安全着陆，仿真与实验验证了策略的有效性。
摘要：
本文提出了用于行星探测场景中动态四足运动的强化学习策略。基于采用五杆腿设计的任务优化四足机器人，我们开发了针对行走、垂直跳跃、前向跳跃和飞行姿态控制的强化学习策略，这些策略明确针对火星上的低重力环境进行了调整。这些策略共同使机器人能够通过协调跳跃和精确的飞行中重新定向来克服比自身更大的障碍物，实现安全着陆。我们通过单轴重新定向测试在Olympus四足机器人上展示了姿态控制策略的Sim2Real迁移，而所有运动策略均在仿真中进行了验证。一个完整的火星探测任务场景展示了在复杂地形上协调策略部署的能力。实验结果显示，在2.6秒内完成90°姿态重新定向，仿真表明在火星重力条件下可实现3.1米的垂直跳跃和3.9米的前向跳跃。- 补充视频：https://www.youtube.com/watch?v=qlSJ3P87A4A
英文摘要：
This paper presents reinforcement learning (RL) policies for dynamic quadrupedal locomotion in planetary exploration scenarios. Building on a taskoptimized quadruped with a 5-bar leg design, we develop RL policies for walking, vertical jumping, forward jumping, and in-flight attitude control, explicitly tailored to the reduced gravity on Mars. These policies jointly enable such robots to overcome obstacles larger than themselves through coordinated jumping and precise in-flight reorientation for safe landings. We demonstrate Sim2Real transfer of the attitude control policy on the Olympus quadruped through single-axis reorientation tests, while all locomotion policies are validated in simulation. A complete Mars exploration mission scenario demonstrates coordinated policy deployment across challenging terrain. Experimental results show 90° attitude reorientation in 2.6 seconds, with simulations demonstrating 3.1 meter vertical jumps and 3.9 meter forward jumps under Martian gravity conditions. - Supplementary video: https://www.youtube.com/watch?v=qlSJ3P87A4A
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
